from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				PegasusConfig
   __lowerCAmelCase           =				{}
   __lowerCAmelCase           =				"""gelu"""







   def __init__(     self						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str=13   ,   lowerCamelCase_						:						Any=7   ,   lowerCamelCase_						:						Dict=True   ,   lowerCamelCase_						:						Any=False   ,   lowerCamelCase_						:						List[str]=99   ,   lowerCamelCase_						:						str=32   ,   lowerCamelCase_						:						str=2   ,   lowerCamelCase_						:						Any=4   ,   lowerCamelCase_						:						Optional[int]=37   ,   lowerCamelCase_						:						List[Any]=0.1   ,   lowerCamelCase_						:						Any=0.1   ,   lowerCamelCase_						:						Optional[Any]=40   ,   lowerCamelCase_						:						Optional[int]=2   ,   lowerCamelCase_						:						int=1   ,   lowerCamelCase_						:						int=0   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      seq_length
    UpperCamelCase			  =      is_training
    UpperCamelCase			  =      use_labels
    UpperCamelCase			  =      vocab_size
    UpperCamelCase			  =      hidden_size
    UpperCamelCase			  =      num_hidden_layers
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      intermediate_size

    UpperCamelCase			  =      hidden_dropout_prob
    UpperCamelCase			  =      attention_probs_dropout_prob
    UpperCamelCase			  =      max_position_embeddings
    UpperCamelCase			  =      eos_token_id
    UpperCamelCase			  =      pad_token_id
    UpperCamelCase			  =      bos_token_id







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length - 1]   ,   self.vocab_size				)
    UpperCamelCase			  =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)   ,   1				)
    UpperCamelCase			  =      tf.concat([input_ids, eos_tensor]   ,   axis=1				)

    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size				)

    UpperCamelCase			  =      self.config_cls(
        vocab_size=self.vocab_size   ,   d_model=self.hidden_size   ,   encoder_layers=self.num_hidden_layers   ,   decoder_layers=self.num_hidden_layers   ,   encoder_attention_heads=self.num_attention_heads   ,   decoder_attention_heads=self.num_attention_heads   ,   encoder_ffn_dim=self.intermediate_size   ,   decoder_ffn_dim=self.intermediate_size   ,   dropout=self.hidden_dropout_prob   ,   attention_dropout=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   eos_token_ids=[2]   ,   bos_token_id=self.bos_token_id   ,   pad_token_id=self.pad_token_id   ,   decoder_start_token_id=self.pad_token_id   ,   **self.config_updates   ,   )
    UpperCamelCase			  =      prepare_pegasus_inputs_dict(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)
    return config, inputs_dict







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      TFPegasusModel(config=lowerCamelCase_				).get_decoder()
    UpperCamelCase			  =      inputs_dict["""input_ids"""]

    UpperCamelCase			  =      input_ids[:1, :]
    UpperCamelCase			  =      inputs_dict["""attention_mask"""][:1, :]
    UpperCamelCase			  =      inputs_dict["""head_mask"""]
    UpperCamelCase			  =      1

    # first forward pass
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   head_mask=lowerCamelCase_   ,   use_cache=lowerCamelCase_				)

    UpperCamelCase  ,     UpperCamelCase			  =      outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    UpperCamelCase			  =      ids_tensor((self.batch_size, 3)   ,   config.vocab_size				)
    UpperCamelCase			  =      tf.cast(ids_tensor((self.batch_size, 3)   ,   2				)   ,   tf.inta				)

    # append to next input_ids and
    UpperCamelCase			  =      tf.concat([input_ids, next_tokens]   ,   axis=-1				)
    UpperCamelCase			  =      tf.concat([attention_mask, next_attn_mask]   ,   axis=-1				)

    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				)[0]
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   past_key_values=lowerCamelCase_				)[0]

    self.parent.assertEqual(next_tokens.shape[1]   ,   output_from_past.shape[1]				)

    # select random slice
    UpperCamelCase			  =      int(ids_tensor((1,)   ,   output_from_past.shape[-1]				)				)
    UpperCamelCase			  =      output_from_no_past[:, -3:, random_slice_idx]
    UpperCamelCase			  =      output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(lowerCamelCase_   ,   lowerCamelCase_   ,   rtol=1E-3				)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					)		->	List[Any]:







 '''simple docstring'''
 if attention_mask is None:
  UpperCamelCase			  =      tf.cast(tf.math.not_equal(UpperCamelCase_							,					config.pad_token_id       )							,					tf.inta       )
 if decoder_attention_mask is None:
  UpperCamelCase			  =      tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape							,					dtype=tf.inta       ),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,					config.pad_token_id       )							,					tf.inta       ),
      ]							,					axis=-1							,					)
 if head_mask is None:
  UpperCamelCase			  =      tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
 if decoder_head_mask is None:
  UpperCamelCase			  =      tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
 if cross_attn_head_mask is None:
  UpperCamelCase			  =      tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }
@require_tf
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   __lowerCAmelCase           =				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   __lowerCAmelCase           =				(
       {
           """conversational""": TFPegasusForConditionalGeneration,
           """feature-extraction""": TFPegasusModel,
           """summarization""": TFPegasusForConditionalGeneration,
           """text2text-generation""": TFPegasusForConditionalGeneration,
           """translation""": TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __lowerCAmelCase           =				True
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      TFPegasusModelTester(self				)
    UpperCamelCase			  =      ConfigTester(self   ,   config_class=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    self.config_tester.run_common_tests()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_				)





@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):
   __lowerCAmelCase           =				[
       """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
       """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
   ]
   __lowerCAmelCase           =				[
       """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
       """ reduce the risk of wildfires.""",
       """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
   ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
   __lowerCAmelCase           =				"""google/pegasus-xsum"""







   @cached_property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    return AutoTokenizer.from_pretrained(self.model_name				)







   @cached_property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
    return model







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   **lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.translate_src_text(**lowerCamelCase_				)
    assert self.expected_text == generated_words







   def 						lowerCamelCase_	(     self						:						List[str]   ,   **lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.tokenizer(self.src_text   ,   **lowerCamelCase_   ,   padding=lowerCamelCase_   ,   return_tensors="""tf"""				)
    UpperCamelCase			  =      self.model.generate(
        model_inputs.input_ids   ,   attention_mask=model_inputs.attention_mask   ,   num_beams=2   ,   use_cache=lowerCamelCase_   ,   )
    UpperCamelCase			  =      self.tokenizer.batch_decode(generated_ids.numpy()   ,   skip_special_tokens=lowerCamelCase_				)
    return generated_words







   @slow
   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    self._assert_generated_batch_equal_expected()

import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
   __lowerCAmelCase           =				field(
       default="""NER"""  ,     metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
   __lowerCAmelCase           =				field(default=__lowerCAmelCase  ,     metadata={"""help""": """Set this flag to use fast tokenization."""}		)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,     )





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,     )
   __lowerCAmelCase           =				field(
       default=128  ,     metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }  ,     )
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)





def 			lowercase(	)		->	Optional[Any]:







 '''simple docstring'''
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCamelCase			  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
 if len(sys.argv       ) == 2 and sys.argv[1].endswith(""".json"""       ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
 else:
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir       )
     and os.listdir(training_args.output_dir       )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      """ --overwrite_output_dir to overcome."""       )

 UpperCamelCase			  =      import_module("""tasks"""       )
 try:
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					model_args.task_type       )
  UpperCamelCase			  =      token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,					datefmt="""%m/%d/%Y %H:%M:%S"""							,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,					)
 logger.warning(
     """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,					training_args.local_rank							,					training_args.device							,					training_args.n_gpu							,					bool(training_args.local_rank != -1       )							,					training_args.fpaa							,					)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank       ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s"""							,					UpperCamelCase_       )

 # Set seed
 set_seed(training_args.seed       )

 # Prepare CONLL-2003 task
 UpperCamelCase			  =      token_classification_task.get_labels(data_args.labels       )
 UpperCamelCase			  =      dict(enumerate(UpperCamelCase_       )       )
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCamelCase			  =      AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,					num_labels=UpperCamelCase_							,					idalabel=UpperCamelCase_							,					labelaid={label: i for i, label in enumerate(UpperCamelCase_       )}							,					cache_dir=model_args.cache_dir							,					)
 UpperCamelCase			  =      AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,					cache_dir=model_args.cache_dir							,					use_fast=model_args.use_fast							,					)
 UpperCamelCase			  =      AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path							,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path       )							,					config=UpperCamelCase_							,					cache_dir=model_args.cache_dir							,					)

 # Get datasets
 UpperCamelCase			  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.train							,					)
     if training_args.do_train
     else None
 )
 UpperCamelCase			  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.dev							,					)
     if training_args.do_eval
     else None
 )

 def align_predictions(UpperCamelCase_							,					UpperCamelCase_       ) -> Tuple[List[int], List[int]]:
  UpperCamelCase			  =      np.argmax(UpperCamelCase_							,					axis=2       )

  UpperCamelCase  ,     UpperCamelCase			  =      preds.shape

  UpperCamelCase			  =      [[] for _ in range(UpperCamelCase_       )]
  UpperCamelCase			  =      [[] for _ in range(UpperCamelCase_       )]

  for i in range(UpperCamelCase_       ):
   for j in range(UpperCamelCase_       ):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     out_label_list[i].append(label_map[label_ids[i][j]]       )
     preds_list[i].append(label_map[preds[i][j]]       )

  return preds_list, out_label_list

 def compute_metrics(UpperCamelCase_       ) -> Dict:
  UpperCamelCase  ,     UpperCamelCase			  =      align_predictions(p.predictions							,					p.label_ids       )
  return {
      "accuracy_score": accuracy_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "precision": precision_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "recall": recall_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "f1": fa_score(UpperCamelCase_							,					UpperCamelCase_       ),
  }

 # Data collator
 UpperCamelCase			  =      DataCollatorWithPadding(UpperCamelCase_							,					pad_to_multiple_of=8       ) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCamelCase			  =      Trainer(
     model=UpperCamelCase_							,					args=UpperCamelCase_							,					train_dataset=UpperCamelCase_							,					eval_dataset=UpperCamelCase_							,					compute_metrics=UpperCamelCase_							,					data_collator=UpperCamelCase_							,					)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_process_zero():
   tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
 UpperCamelCase			  =      {}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""       )

  UpperCamelCase			  =      trainer.evaluate()

  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""eval_results.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    logger.info("""***** Eval results *****"""       )
    for key, value in result.items():
     logger.info("""  %s = %s"""							,					UpperCamelCase_							,					UpperCamelCase_       )
     writer.write("""%s = %s\n""" % (key, value)       )

   results.update(UpperCamelCase_       )

    # Predict
 if training_args.do_predict:
  UpperCamelCase			  =      TokenClassificationDataset(
      token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.test							,					)

  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      trainer.predict(UpperCamelCase_       )
  UpperCamelCase  ,     UpperCamelCase			  =      align_predictions(UpperCamelCase_							,					UpperCamelCase_       )

  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""test_results.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    for key, value in metrics.items():
     logger.info("""  %s = %s"""							,					UpperCamelCase_							,					UpperCamelCase_       )
     writer.write("""%s = %s\n""" % (key, value)       )

        # Save predictions
  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""test_predictions.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    with open(os.path.join(data_args.data_dir							,					"""test.txt"""       )							,					"""r"""       ) as f:
     token_classification_task.write_predictions_to_file(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 return results





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()
